In a power plant, non-electrical energy, for example in the form of fossil fuels, is converted into electrical energy and a power network is provided. Depending on the type of raw material used for the generation of electrical energy, a differentiation is made for example between coal-fired power plants, nuclear power plants, gas and steam turbine power plants etc.
Due to the internationally increasing demand for energy and the shortage of fossil fuel primary energy sources, the price of the major raw fuels used for conversion into electricity is currently rising. In addition, there are increasingly strict environmental requirements relating to fine dust, NOx, SO2 and CO2. Therefore, attempts are being made to increase the efficiency of power plants, i.e. improve their operational performance.
In addition to cost-intensive development and the renewal of plant components, modern process control technology can also help to optimize process management taking into account the current boundary conditions. Here, different optimization criteria may be required, such as, for example, increased efficiency or reduced pollutant emissions. In this regard, decisions which were traditionally based on the experience of the operating personnel can nowadays be reached with the aid of computers and corresponding methods based on physical mathematical models of the plant power process.
Usually, a method of this kind includes a target function which uses a physical model of the power plant in question to generate a scalar or vector-valued function value, for example, from a set of process values. Hereby, the process values include, on the one hand, values determined by external influences (environment variables) such as, for example, ambient and cooling-water temperature, and which change during operation. Therefore, these environment variables represent current boundary conditions which cannot be influenced, but which do exert an influence on the process.
On the other hand, the process values also include manipulated variables such as, for example, the position of an actuator or valve or the quantity of fuel supplied, which can be influenced by the operating personnel or an automated control device during the operation of the power plant, i.e. process or state variables that are freely selectable within certain limits. Each set of variables in conjunction with the environment variables produces a target function value which can be used to evaluate the relevant set and it is usual to select the set of variables for transmission to a control device of the power plant whose assigned functional value complies with a predefined optimization criterion. In the case of a scalar function value, this can be, for example, the highest or smallest functional value.
In order to find an optimum set of variables for controlling the power plant, it is usual to use gradient methods to find a minimum or maximum for the target function. Various methods are known for this, for example, the method of steepest descent, the (quasi-)Newton method, sequential quadratic programming or the simplex algorithm. Common to all gradient methods is that a local maximum or minimum of the target function is found on the basis of a starting value.
Physical models of power plants, from which the target function for optimization is obtained, are generally not linear and generally not convex. Depending upon the selected starting value, therefore, under some circumstances, the gradient method can find a local maximum or minimum, i.e. locally optimized power plant operating conditions, but this does not guarantee that globally optimum operating conditions have also been found at the same time.